The present invention relates generally to a global positioning system and inertial measurement unit (GPS/IMU) integrated positioning and navigation method and system, and more particularly to a real-time fully-coupled integration method and system of the global positioning system (GPS) receiver and the inertial measurement unit (IMU), which allows the mutual aiding operation of the GPS receiver and the inertial navigation system (INS) at an advanced level with features of inertial aiding global positioning system satellite signal tracking, and on-the-fly resolution of GPS carrier phase integer ambiguities and real-time positioning in the differential GPS mode.
The GPS equipment, which comprises an antenna, a signal processing unit, associated electronics, and displays, receives the signals from the GPS satellites to obtain position, velocity, and time solutions. There are two types of GPS observable: code pseudoranges and carrier phases. Phase measurement is based on two L-band carrier frequencies. One is the L1 carrier with frequency 1575.42 MHz and the other is the L2 carrier with frequency 1227.60 MHz. For pseudorange measurement, there are two basic types of Pseudo Random Noise (PRN) code measurement. One is known as the C/A (Coarse/Acquisition) code modulated on the L1 frequency only and the other is known as the P (Precise) code modulated on both L1 and L2 frequencies. In addition to the above information in the GPS signals, the GPS signals also modulate the navigation message, which includes GPS time, clock corrections, broadcast ephemerides, and system status, on both L1 and L2 frequencies.
Because of the navigation message transmitted by the GPS satellites, the positions and velocities of the GPS satellites can be computed. Therefore, the propagating time of a GPS signal can be determined. Since the signal travels at the speed of light, the user can calculate the geometrical range to the satellite. In this way, the code pseudorange measurements can be determined and is degraded by errors, such as ephemeris errors, user and satellite clock biases (including selective availability (SA)), atmospheric effects (ionosphere and troposphere), and measurement noise (receiver error and random noise). These errors not only affect pseudorange measurement but phase measurement. The most obvious difference between both measurements is the measurement error. For phase measurement, the measurement noise is of the order of a few millimeters and for pseudorange measurement that is accurate to about 30 centimeters (for the P code) or 3 meters (for the C/A code).
In addition to the unavoidable errors (such as ionospheric delay, tropospheric delay, clock biases, and measurement errors) and the intentional error (such as SA). the GPS measurements (pseudorange and phase) may also be affected by the environment surrounding a GPS user antenna. Like the multipath effect, because of an object nearby the user antenna, the antenna receives not only a direct signal from a GPS satellite but also a second or more reflected or diffracted signals from the object. For a highly dynamic vehicle, the onboard GPS receiver may lose the lock of a GPS signal because the signal-to-noise ratio (SNR) is low or the GPS signal is blocked by the body of its own vehicle.
Typically, the navigation solution is estimated by using the pseudorange measurements. Since the satellite clock biases are provided by the navigation message. for three-dimensional position determination, in addition to the three unknowns in position, the receiver (user) clock bias also needs to be estimated. i.e. there are four unknowns for the navigation solution. As a result, for a stand-alone receiver, the position determination usually needs a minimum of four visible GPS satellites, and the estimated position is accurate to about 100 meters with SA on. In order to improve the accuracy of the estimated position, the phase measurements will be used. Also, to eliminate the most of SA and other common errors (for example, receiver and satellite clock biases), the differential GPS will be employed. As a result, the accuracy of the estimated position is of the order of a few centimeters. However, to achieve the centimeter accuracy, one of key steps is to resolve carrier phase integer ambiguities.
An inertial navigation system (INS) comprises an onboard inertial measurement unit (IMU), a processor, and embedded navigation software(s), where the components of the IMU include the inertial sensors (accelerometers and gyros) and the associated hardware and electronics. Based on the measurements of vehicle specific forces and rotation rates obtained from onboard inertial sensors, the positioning solution is obtained by numerically solving Newton""s equations of motion.
The inertial navigation system is. in general. classified as a gimbaled configuration and a strapdown configuration. For a gimbled inertial navigation system, the accelerometers and gyros are mounted on a gimbaled platform to isolate the sensors from the rotations of the vehicle and then to keep the measurements and navigation calculations in a stabilized navigation coordinate frame. Generally, the motion of the vehicle can be expressed in several navigation frames of reference, such as earth centered inertial (ECI), earth-centered earth-fixed (ECEF), locally level with axes in the directions of north-east-down (NED), and locally level with a wander azimuth. For a strapdown inertial navigation system, the inertial sensors are rigidly mounted to the vehicle body frame. In order to perform the navigation computation in the stabilized navigation frame. a coordinate frame transformation matrix is used to transform the acceleration and rotation measurements gimbled from the body frame to one of the navigation frames.
In general, the measurements from the gimbled inertial navigation system are more accurate than the ones from the strapdown inertial navigation system. And, the gimbled inertial navigation system is easier in calibration than the strapdown inertial navigation system. However, the strapdown inertial navigation systems are more suitable for higher dynamic conditions (such as high turn rate maneuvers) which can stress inertial sensor performance. Also, with the availability of modern gyros and accelerometers, the strapdown inertial navigation systems become the predominant mechanization due to their low cost and reliability.
Inertial navigation systems, in principle, permit pure autonomous operation and output continuous position, velocity, and attitude data of the vehicle after initializing the starting position and initiating an alignment procedure. In addition to autonomous operation, other advantages of an inertial navigation system include the full navigation solution and wide bandwidth. However, an inertial navigation system is expensive and degraded with drift in output (position and velocity) over an extended period of time. It means that the position and velocity errors increase with time. This error propagation characteristic is primarily caused by, such as, gyro drift, accelerometer bias, misalignment, gravity disturbance, initial position and velocity errors, and scale factor errors.
Under the requirements, such as low cost, high accuracy, continuous output, high degree of resistance to jamming, and high dynamics, the stand-alone INS and stand-alone GPS have difficulties to perform properly. Therefore, to decrease or diminish the drawbacks for each system (INS and GPS), the integration of both systems is one of the ways to achieve the above requirements. In general, there are three conventional approaches for integrating the GPS and INS. The first approach is to reset directly the INS with the GPS-derived position and velocity. The second approach is the cascaded integration where the GPS-derived position and velocity are used as the measurements in an integration Kalman filter. The third approach is to use an extended Kalman filter which processes the GPS raw pseudorange and delta rang,e measurements to provide optimal error estimates of navigation parameters, such as the inertial navigation system, inertial sensor errors, and the global positioning system receiver clock offset.
However, there are some shortcomings of the above existing integration approaches and they are summarized as follows:
1. In the conventional global positioning system and inertial navigation system integration approaches, only position and velocity from the output of the GPS receiver or the GPS raw pseudorange and delta range measurements are used. However, the GPS raw phase measurements haven""t been used for an integration solution although the phase measurements are accurate to a few millimeters in contrast to 30 centimeters for the P code pseudorange or 3 meters for the C/A code pseudorange in the presence of measurement noise.
2. There is a significant impediment to the aiding of the global positioning system signal tracking loops with an inertial navigation system. That is, the aiding causes the potential instability of the conventional global positioning system and inertial navigation integration system because of a positive feedback signal loop in the integrated global positioning and inertial system. As a result, the degradation in accuracy of the inertial aiding data increases the signal tracking errors. And, the increased tracking errors are fed back into the inertial system. This may cause further degradation of the inertial system because the measurements may severely affect the Kalman filter, which is well tuned for a low accuracy inertial navigation system.
3. The inertial sensors in the conventional tightly-coupled GPS and inertial integration system can not provide the high accuracy in velocity. Therefore, the aiding of a carrier phase tracking loop can not execute properly due to the need for high accuracy of the external input velocity.
An objective of the present invention is to use the velocity and acceleration from an inertial navigation processor, which are corrected by a Kalman filter, as the aiding of the code and carrier phase tracking of the GPS satellite signals so as to enhance the performance of the GPS/INS, even in heavy jamming and high dynamic environments.
Another objective of the present invention is to improve the accuracy of the receiver position and velocity by using differential GPS. To accurately determine the receiver position and velocity at the centimeter level, the GPS phase measurements will be used and the differential GPS will be employed. In this invention, a new process (OTF (on-the-fly) technique) is disclosed to resolve the integer ambiguities on the fly and estimate the receiver position in real time. The results of GPS estimates will increase the accuracy of the inertial navigation system and therefore enhance the capability of the GPS tracking loop.
Another objective of the present invention is that the self-contained INS complements the GPS as the GPS receiver loses lock of the GPS signals. Once the GPS receiver regains the signals and then estimates the receiver position and velocity, the output (position and velocity) of the GPS receiver is used to correct the position and velocity of the INS that are drifted.
Another objective of the present invention is that a data link is used to receive the data, such as position, velocity, and raw measurements, from a reference site in addition to a GPS receiver to collect the raw measurements for a rover site. Using the differential GPS and phase measurements, the accuracy of the GPS positioning is of the order of centimeter level after fixing the integer ambiguities. As a result, the integrated GPS/INS is applicable in high accuracy positioning.
A further objective of the present invention is that the inertial navigation system can aid the resolution of the GPS carrier phase integer ambiguities by providing more accurate position information.
Another objective of the present invention is that the Kalman filter processes the GPS phase measurements as well as the GPS pseudorange and delta range from both reference and rover sites, so as to improve the accuracy of the integrated positioning solution.
Another objective of the present invention is that the Kalman filter is implemented in real time to optimally blend the GPS raw data and the INS solution and to estimate the navigation solution.
Another further objective of the present invention is that a robust Kalman filter is implemented in real time to eliminate the possible instability of the integration solution.
Another objective of the present invention is that a low accuracy inertial sensor is used to achieve a high accuracy integration solution by the aid of the global positioning system measurement.
Another objective of the present invention is to provide a real-time integrated vehicle positioning method, which can substantially solve the problem of instability present in many existing systems where a Kalman filter is used to perform optimal estimation.
Another objective of the present invention is to provide a real-time integrated vehicle positioning method, which supports high precision navigation in general aviation and space applications. It can also be used for ground motion vehicles tracking and navigation applications.
Another objective of the present invention is to provide a real-time integrated vehicle positioning method, which uses the GPS raw phase measurements to update the inertial navigation system and aids the GPS tracking loop by the accurate output of the inertial navigation system so as to satisfy the requirements of, such as low cost, high accuracy, continuous output, high degree of resistance to jamming, and high dynamics, and to overcome the disadvantages of the existing techniques.